{"id":"https://openalex.org/W2110615995","doi":"https://doi.org/10.1007/s00180-009-0165-9","title":"Penalized multinomial mixture logit model","display_name":"Penalized multinomial mixture logit model","publication_year":2009,"publication_date":"2009-08-13","ids":{"openalex":"https://openalex.org/W2110615995","doi":"https://doi.org/10.1007/s00180-009-0165-9","mag":"2110615995"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-009-0165-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-009-0165-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-009-0165-9.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-009-0165-9.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112166127","display_name":"Shaheena Bashir","orcid":null},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Shaheena Bashir","raw_affiliation_strings":["Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4L8, Canada","McMaster University, Department of Mathematics and Statistics, L8S 4L8, Hamilton, ON, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4L8, Canada","institution_ids":["https://openalex.org/I98251732"]},{"raw_affiliation_string":"McMaster University, Department of Mathematics and Statistics, L8S 4L8, Hamilton, ON, Canada#TAB#","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014238561","display_name":"Edward M. Carter","orcid":null},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Edward M. Carter","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Guelph, Guelph, ON, N1G 2W1, Canada","University of Guelph, Department of Mathematics and Statistics, N1G 2W1, Guelph, ON, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Guelph, Guelph, ON, N1G 2W1, Canada","institution_ids":["https://openalex.org/I79817857"]},{"raw_affiliation_string":"University of Guelph, Department of Mathematics and Statistics, N1G 2W1, Guelph, ON, Canada#TAB#","institution_ids":["https://openalex.org/I79817857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112166127"],"corresponding_institution_ids":["https://openalex.org/I98251732"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.4362,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73239033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"25","issue":"1","first_page":"121","last_page":"141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.7758716344833374},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7654394507408142},{"id":"https://openalex.org/keywords/multinomial-probit","display_name":"Multinomial probit","score":0.6122426390647888},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5920006036758423},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5751842260360718},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5715650320053101},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5416533946990967},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5232952237129211},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5000209808349609},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.47135820984840393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4673158526420593},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.44787412881851196},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43411898612976074},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4336153566837311},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.4281364977359772},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.39878571033477783},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3427053689956665}],"concepts":[{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.7758716344833374},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7654394507408142},{"id":"https://openalex.org/C46704056","wikidata":"https://www.wikidata.org/wiki/Q17086346","display_name":"Multinomial probit","level":3,"score":0.6122426390647888},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5920006036758423},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5751842260360718},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5715650320053101},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5416533946990967},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5232952237129211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5000209808349609},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.47135820984840393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4673158526420593},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.44787412881851196},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43411898612976074},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4336153566837311},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4281364977359772},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.39878571033477783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3427053689956665},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00180-009-0165-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-009-0165-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-009-0165-9.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:compst:v:25:y:2010:i:1:p:121-141","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1007/s00180-009-0165-9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s00180-009-0165-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-009-0165-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-009-0165-9.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.699999988079071,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2110615995.pdf","grobid_xml":"https://content.openalex.org/works/W2110615995.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W191383808","https://openalex.org/W1528905581","https://openalex.org/W1550812296","https://openalex.org/W1614659291","https://openalex.org/W1966701961","https://openalex.org/W1969557815","https://openalex.org/W1970502168","https://openalex.org/W1981078482","https://openalex.org/W2002374079","https://openalex.org/W2012877697","https://openalex.org/W2013416277","https://openalex.org/W2033135151","https://openalex.org/W2035983506","https://openalex.org/W2042488013","https://openalex.org/W2045980827","https://openalex.org/W2046649434","https://openalex.org/W2047028564","https://openalex.org/W2049633694","https://openalex.org/W2063009186","https://openalex.org/W2078129874","https://openalex.org/W2079775628","https://openalex.org/W2109363337","https://openalex.org/W2117812871","https://openalex.org/W2121407732","https://openalex.org/W2135046866","https://openalex.org/W2139766869","https://openalex.org/W2147503863","https://openalex.org/W2333269907","https://openalex.org/W4214737323","https://openalex.org/W4234698323","https://openalex.org/W4235844763","https://openalex.org/W4241342703","https://openalex.org/W4300956553","https://openalex.org/W4301861531"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2044228987","https://openalex.org/W2113920489","https://openalex.org/W2362114017","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W2027354766","https://openalex.org/W2112968255","https://openalex.org/W2088940513"],"abstract_inverted_index":{"Normal":[0],"distribution":[1,58],"based":[2,17,87],"discriminant":[3,20,37,113,146],"methods":[4],"have":[5],"been":[6],"used":[7],"for":[8,77],"the":[9,24,30,57,60,82,110,117,129,144],"classification":[10],"of":[11,39,59,66,81,119,132],"new":[12,73],"entities":[13],"into":[14],"different":[15],"groups":[16,31],"on":[18,88],"a":[19,52,64,72,125],"rule":[21],"constructed":[22],"from":[23],"learning":[25,83],"set.":[26],"In":[27,69],"practice":[28],"if":[29],"are":[32],"not":[33],"homogeneous,":[34],"then":[35],"mixture":[36,65,91,112,138],"analysis":[38,114],"Hastie":[40],"and":[41],"Tibshirani":[42],"(J":[43],"R":[44],"Stat":[45],"Soc":[46],"Ser":[47],"B":[48],"58(1):155\u2013176,":[49],"1996)":[50],"is":[51,63,85,96],"useful":[53],"approach,":[54],"assuming":[55],"that":[56],"feature":[61],"vectors":[62],"multivariate":[67],"normals.":[68],"this":[70,135],"paper":[71],"logistic":[74],"regression":[75],"model":[76,140],"heterogenous":[78],"group":[79],"structure":[80],"set":[84],"proposed":[86],"penalized":[89,136],"multinomial":[90,137],"logit":[92,139],"models.":[93],"This":[94,122],"approach":[95,115],"shown":[97],"through":[98],"simulation":[99],"studies":[100],"to":[101,143,155],"be":[102],"more":[103],"effective.":[104],"The":[105],"results":[106,152],"were":[107],"compared":[108,142],"with":[109],"standard":[111],"using":[116,134],"probability":[118,131],"misclassification":[120,133],"criterion.":[121],"comparison":[123],"showed":[124,150],"slight":[126],"reduction":[127],"in":[128],"average":[130],"as":[141],"classical":[145],"rules.":[147],"It":[148],"also":[149],"better":[151],"when":[153],"applied":[154],"practical":[156],"life":[157],"data":[158],"problems":[159],"producing":[160],"smaller":[161],"errors.":[162]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
